Comparing Partial Least Square Approaches in Gene-or Region-based Association Study for Multiple Quantitative Phenotypes

On thinking quantitatively of complex diseases, there are at least three statistical strategies for association study: single SNP on single trait, gene-or region (with multiple SNPs) on single trait and on multiple traits. The third of which is the most general in dissecting the genetic mechanism underlying complex diseases underpinning multiple quantitative traits. Gene-or region association methods based on partial least square (PLS) approaches have been shown to have apparent power advantage. However, few attempts are developed for multiple quantitative phenotypes or traits underlying a condition or disease, and the performance of various PLS approaches used in association study for multiple quantitative traits had not been assessed. We, from regression perspective, exploit association between multiple SNPs and multiple phenotypes or traits through exhaustive scan statistics (sliding window) using PLS and sparse PLS (SPLS) regression. Simulations are conducted to assess the performance of the proposed scan statistics and compare them with the existed method. The proposed methods are applied to 12 regions of GWAS data from the European Prospective Investigation of Cancer (EPIC)-Norfolk study

Gene- or region-based association study via kernel principal component analysis.

BACKGROUND: In genetic association study, especially in GWAS, gene- or region-based methods have been more popular to detect the association between multiple SNPs and diseases (or traits). Kernel principal component analysis combined with logistic regression test (KPCA-LRT) has been successfully used in classifying gene expression data. Nevertheless, the purpose of association study is to detect the correlation between genetic variations and disease rather than to classify the sample, and the genomic data is categorical rather than numerical. Recently, although the kernel-based logistic regression model in association study has been proposed by projecting the nonlinear original SNPs data into a linear feature space, it is still impacted by multicolinearity between the projections, which may lead to loss of power. We, therefore, proposed a KPCA-LRT model to avoid the multicolinearity. RESULTS: Simulation results showed that KPCA-LRT was always more powerful than principal component analysis combined with logistic regression test (PCA-LRT) at different sample sizes, different significant levels and different relative risks, especially at the genewide level (1E-5) and lower relative risks (RR = 1.2, 1.3). Application to the four gene regions of rheumatoid arthritis (RA) data from Genetic Analysis Workshop16 (GAW16) indicated that KPCA-LRT had better performance than single-locus test and PCA-LRT. CONCLUSIONS: KPCA-LRT is a valid and powerful gene- or region-based method for the analysis of GWAS data set, especially under lower relative risks and lower significant levels.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Headwater streams contain amounts of heavy metal in an alpine forest in the upper reaches of the Yangtze River

Headwater streams are an essential link in the source and sink dynamics of heavy metals between terrestrial and aquatic ecosystems and are also critically important for downstream ecosystem processes and water quality. However, there is little available information about headwater streams. Therefore, the stream storage and distribution patterns of Cd, Pb, Ni, Cr, Cu, Mn and Zn were investigated in ten headwater streams of an Alpine forest located in the upper Yangtze River during the rainy season. The results indicated that the heavy metal storage per unit area of the investigated streams was as follows: 0.95 mg·m-2 for Cd, 8.36 mg m-2 for Pb, 1.98 mg m-2 for Ni, 136.98 mg m-2 for Cr, 9.29 mg m-2 for Cu, 433.39 mg m-2 for Mn and 29.07 mg m-2 for Zn; while the heavy metal storage per unit area of the catchment was as follows: 1.19 mg hm-2 for Cd, 10.47 mg hm-2 for Pb, 2.48 mg hm-2 for Ni, 171.62 mg hm-2 for Cr, 11.64 mg hm-2 for Cu, 542.99 mg hm-2 for Mn and 36.42 mg hm-2 for Zn. Headwater streams present remarkable potential for contamination, and plant debris from riparian forests may be the most important source of heavy metals, while the stream sediment acts as a significant sink for heavy metals. These results provide new perspectives and data for understanding the ecological links between alpine forests and watersheds

Профилактика неотложных состояний на стоматологическом приеме

неотложные состояниястоматологическая помощ

Increasing vertical resolution in US models to improve track forecasts of Hurricane Joaquin with HWRF as an example

The atmosphere−ocean coupled Hurricane Weather Research and Forecast model (HWRF) developed at the National Centers for Environmental Prediction (NCEP) is used as an example to illustrate the impact of model vertical resolution on track forecasts of tropical cyclones. A number of HWRF forecasting experiments were carried out at different vertical resolutions for Hurricane Joaquin, which occurred from September 27 to October 8, 2015, in the Atlantic Basin. The results show that the track prediction for Hurricane Joaquin is much more accurate with higher vertical resolution. The positive impacts of higher vertical resolution on hurricane track forecasts suggest that National Oceanic and Atmospheric Administration/NCEP should upgrade both HWRF and the Global Forecast System to have more vertical levels

Effects of Functionalized Graphene Nanoplatelets on the Morphology and Properties of Phenolic Resins

Graphene nanoplatelets (Gnps) were covalently functionalized by 3-aminopropyltriethoxysilane (KH550) and noncovalently functionalized by Triton X-100, respectively. The morphology and structure of KH550 modified graphene (K-Gnp) and Triton X-100 modified graphene (T-Gnp) were characterized by Fourier transform infrared spectroscopy, scanning electron micrograph, and Raman spectrometer. The influences of K-Gnp and T-Gnp on thermal conductivity, fracture toughness, and thermal stability of the boron phenolic resin (BPR) were investigated. Both covalently functionalized K-Gnp and noncovalently functionalized T-Gnp not only improve the dispersion of Gnp in the polymer matrix but also increase interfacial bonding strength between the BPR matrix and Gnp, thus leading to the enhanced mechanical property and thermal stability of nanocomposites. Besides this, mechanical property and thermal stability of the BPR containing K-Gnp are superior to those of BPR containing T-Gnp

Formation of forest gaps accelerates C, N and P release from foliar litter during 4 years of decomposition in an alpine forest

Relative to areas under canopy, the soils in forest gaps receive more irradiance and rainfall (snowfall); this change in microclimate induced by forest gaps may influence the release of carbon (C) and nutrients during litter decomposition. However, great uncertainty remains about the effects of forest gaps on litter decomposition. In this study, we incubated foliar litters from six tree and shrub species in forest gaps and canopy plots and measured the release of C, nitrogen (N) and phosphorus (P) in different snow cover periods in an alpine forest from 2012 to 2016. We found that N was retained by 24-46% but that P was immediately released during an early stage of decomposition. However, forest gaps decreased litter N retention, resulting in more N and P being released from decomposing litters for certain species (i.e., larch, birch and willow litters). Moreover, the release of C and nutrients during litter decomposition stimulated by forest gaps was primarily driven by warmer soil temperature in this high-altitude forest. We conclude that gap formation during forest regeneration may accelerate C turnover and nutrient cycling and that this stimulation might be regulated by the litter species in this seasonally snow-covered forest.Peer reviewe

Trajectories of Haemoglobin and incident stroke risk: a longitudinal cohort study.

BACKGROUND: Studies have demonstrated that high or low haemoglobin increases the risk of stroke. Previous studies, however, performed only a limited number of haemoglobin measurements, while there are dynamic haemoglobin changes over the course of a lifetime. This longitudinal cohort study aimed to classify the long-term trajectory of haemoglobin and examine its association with stroke incidence. METHODS: The cohort consisted of 11,431 participants (6549 men) aged 20 to 50 years whose haemoglobin was repeatedly measured 3-9 times during 2004-2015. A latent class growth mixture model (LCGMM) was used to classify the long-term trajectory of haemoglobin concentrations, and hazard ratios (HRs) and 95% confidence intervals (95% CI) according to the Cox proportional hazard model were used to investigate the association of haemoglobin trajectory types with the risk of stroke. RESULTS: Three distinct trajectory types, high-stable (n = 5395), normal-stable (n = 5310), and decreasing (n = 726), were identified, with stroke incidence rates of 2.7, 1.9 and 3.2 per 1000 person-years, respectively. Compared to the normal-stable group, after adjusting for the baseline covariates, the decreasing group had a 2.94-fold (95% CI 1.22 to 7.06) increased risk of developing stroke. Strong evidence was observed in men, with an HR (95% CI) of 4.12 (1.50, 11.28), but not in women (HR = 1.66, 95% CI 0.34, 8.19). Individuals in the high-stable group had increased values of baseline covariates, but the adjusted HR (95% CI), at 1.23 (0.77, 1.97), was not significant for the study cohort or for men and women separately. CONCLUSIONS: This study revealed that a decreasing haemoglobin trajectory was associated with an increased risk of stroke in men. These findings suggest that long-term decreasing haemoglobin levels might increase the risk of stroke